B.4 Comparison against Discrete Laplace Figure 12 compares Skellam and (discrete) Gaussian against the discrete Laplace mechanism under various accounting schemes on privacy compositions.

Interpreting the meaning of legal open-textured terms is a key task of legal professionals. An important source for this interpretation is how the term was applied in previous court cases. In this paper, we evaluate the performance of GPT-4 in generating factually accurate, clear and relevant explanations of terms in legislation. We compare the performance of a baseline setup, where GPT-4 is directly asked to explain a legal term, to an augmented approach, where a legal information retrieval module is used to provide relevant context to the model, in the form of sentences from case law. We found that the direct application of GPT-4 yields explanations that appear to be of very high quality on their surface. However, detailed analysis uncovered limitations in terms of the factual accuracy of the explanations. Further, we found that the augmentation leads to improved quality, and appears to eliminate the issue of hallucination, where models invent incorrect statements. These findings open the door to the building of systems that can autonomously retrieve relevant sentences from case law and condense them into a useful explanation for legal scholars, educators or practicing lawyers alike.

Numerous deep learning based methods have been developed for nuclei segmentation for H&E images and have achieved close to human performance. However, direct application of such methods to another modality of images, such as Immunohistochemistry (IHC) images, may not achieve satisfactory performance. Thus, we developed a Generative Adversarial Network (GAN) based approach to translate an IHC image to an H&E image while preserving nuclei location and morphology and then apply pre-trained nuclei segmentation models to the virtual H&E image. We demonstrated that the proposed methods work better than several baseline methods including direct application of state of the art nuclei segmentation methods such as Cellpose and HoVer-Net, trained on H&E and a generative method, DeepLIIF, using two public IHC image datasets.

Graph embedding techniques, which learn low-dimensional representations of a graph, are achieving state-of-the-art performance in many graph mining tasks. Most existing embedding algorithms assign a single vector to each node, implicitly assuming that a single representation is enough to capture all characteristics of the node. However, across many domains, it is common to observe pervasively overlapping community structure, where most nodes belong to multiple communities, playing different roles depending on the contexts. Here, we propose persona2vec, a graph embedding framework that efficiently learns multiple representations of nodes based on their structural contexts. Using link prediction-based evaluation, we show that our framework is significantly faster than the existing state-of-the-art model while achieving better performance.

The promise of big data and artificial intelligence is everywhere. And, in all cases, so are the results. One almost gets the impression that there is no problem that cannot be solved with these new technologies. The answer to everything is'big data and artificial intelligence'. Open a web browser and you see advertising tuned to your latest online shopping.

The promise of big data and artificial intelligence is everywhere. And, in all cases, so are the results. One almost gets the impression that there is no problem that cannot be solved with these new technologies. The answer to everything is'big data and artificial intelligence'. Open a web browser and you see advertising tuned to your latest online shopping.

Since Robert Solow, economists know that technical change is the most important force to have driven economic growth in advanced economies. One could argue that this should settle the debate about the effects of artificial intelligence (AI)--it should already be crowned the best invention since electricity. Sadly, access to electricity killed millions of jobs and the fear that AI will do the same has crippled the enthusiasm for it. AI is going to radically improve productivity and welfare in ways such as accelerating vaccine development, improving medical diagnostics, increasing highway safety and reducing traffic congestion. But much of the public debate around it has focussed on the labour market effects of increasing automation.